Overview

Brought to you by YData

Dataset statistics

Number of variables52
Number of observations61045
Missing cells0
Missing cells (%)0.0%
Total size in memory24.2 MiB
Average record size in memory416.0 B

Variable types

Text39
Numeric12
DateTime1

Alerts

examide has constant value "No" Constant
citoglipton has constant value "No" Constant
split has constant value "train" Constant
load_date has constant value "2025-04-29" Constant
number_emergency is highly skewed (γ1 = 24.90032669) Skewed
encounter_id has unique values Unique
num_procedures has 28003 (45.9%) zeros Zeros
number_outpatient has 50954 (83.5%) zeros Zeros
number_emergency has 54209 (88.8%) zeros Zeros
number_inpatient has 40539 (66.4%) zeros Zeros

Reproduction

Analysis started2025-04-29 13:53:31.749342
Analysis finished2025-04-29 13:53:36.049795
Duration4.3 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

encounter_id
Text

Unique 

Distinct61045
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:36.391254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.679465968
Min length5

Characters and Unicode

Total characters529838
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61045 ?
Unique (%)100.0%

Sample

1st row2278392
2nd row500364
3rd row16680
4th row35754
5th row55842
ValueCountFrequency (%)
2278392 1
 
< 0.1%
150048 1
 
< 0.1%
182796 1
 
< 0.1%
16680 1
 
< 0.1%
35754 1
 
< 0.1%
55842 1
 
< 0.1%
28236 1
 
< 0.1%
42570 1
 
< 0.1%
62256 1
 
< 0.1%
73578 1
 
< 0.1%
Other values (61035) 61035
> 99.9%
2025-04-29T13:53:37.019982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 67026
12.7%
1 65099
12.3%
4 57449
10.8%
6 56487
10.7%
8 54570
10.3%
0 53925
10.2%
3 47290
8.9%
5 43617
8.2%
7 42747
8.1%
9 41628
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 529838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 67026
12.7%
1 65099
12.3%
4 57449
10.8%
6 56487
10.7%
8 54570
10.3%
0 53925
10.2%
3 47290
8.9%
5 43617
8.2%
7 42747
8.1%
9 41628
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 529838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 67026
12.7%
1 65099
12.3%
4 57449
10.8%
6 56487
10.7%
8 54570
10.3%
0 53925
10.2%
3 47290
8.9%
5 43617
8.2%
7 42747
8.1%
9 41628
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 529838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 67026
12.7%
1 65099
12.3%
4 57449
10.8%
6 56487
10.7%
8 54570
10.3%
0 53925
10.2%
3 47290
8.9%
5 43617
8.2%
7 42747
8.1%
9 41628
7.9%

patient_nbr
Real number (ℝ)

Distinct47663
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54251237.41
Minimum135
Maximum189502619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:37.235542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1451637
Q123415390
median45342639
Q387446673
95-th percentile111339761.4
Maximum189502619
Range189502484
Interquartile range (IQR)64031283

Descriptive statistics

Standard deviation38606610.09
Coefficient of variation (CV)0.7116263504
Kurtosis-0.3476298233
Mean54251237.41
Median Absolute Deviation (MAD)32784120
Skewness0.4703017285
Sum3.311766788 × 1012
Variance1.490470342 × 1015
MonotonicityNot monotonic
2025-04-29T13:53:37.492849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88785891 27
 
< 0.1%
43140906 16
 
< 0.1%
1660293 15
 
< 0.1%
88479036 14
 
< 0.1%
23643405 14
 
< 0.1%
41699412 13
 
< 0.1%
84676248 13
 
< 0.1%
23199021 13
 
< 0.1%
92709351 12
 
< 0.1%
89472402 12
 
< 0.1%
Other values (47653) 60896
99.8%
ValueCountFrequency (%)
135 2
< 0.1%
378 1
 
< 0.1%
774 1
 
< 0.1%
927 1
 
< 0.1%
1152 4
< 0.1%
ValueCountFrequency (%)
189502619 1
< 0.1%
189365864 1
< 0.1%
189349430 1
< 0.1%
189332087 1
< 0.1%
189298877 1
< 0.1%

race
Text

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:37.678906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length9
Mean length9.863117372
Min length1

Characters and Unicode

Total characters602094
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaucasian
2nd rowCaucasian
3rd rowCaucasian
4th rowCaucasian
5th rowCaucasian
ValueCountFrequency (%)
caucasian 45581
74.7%
africanamerican 11626
 
19.0%
1334
 
2.2%
hispanic 1207
 
2.0%
other 897
 
1.5%
asian 400
 
0.7%
2025-04-29T13:53:37.981570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 161602
26.8%
i 71647
11.9%
n 70440
11.7%
c 70040
11.6%
s 47188
 
7.8%
C 45581
 
7.6%
u 45581
 
7.6%
r 24149
 
4.0%
A 23652
 
3.9%
e 12523
 
2.1%
Other values (8) 29691
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 602094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 161602
26.8%
i 71647
11.9%
n 70440
11.7%
c 70040
11.6%
s 47188
 
7.8%
C 45581
 
7.6%
u 45581
 
7.6%
r 24149
 
4.0%
A 23652
 
3.9%
e 12523
 
2.1%
Other values (8) 29691
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 602094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 161602
26.8%
i 71647
11.9%
n 70440
11.7%
c 70040
11.6%
s 47188
 
7.8%
C 45581
 
7.6%
u 45581
 
7.6%
r 24149
 
4.0%
A 23652
 
3.9%
e 12523
 
2.1%
Other values (8) 29691
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 602094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 161602
26.8%
i 71647
11.9%
n 70440
11.7%
c 70040
11.6%
s 47188
 
7.8%
C 45581
 
7.6%
u 45581
 
7.6%
r 24149
 
4.0%
A 23652
 
3.9%
e 12523
 
2.1%
Other values (8) 29691
 
4.9%

gender
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:38.105875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length6
Mean length5.080563519
Min length4

Characters and Unicode

Total characters310143
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale
ValueCountFrequency (%)
female 32965
54.0%
male 28077
46.0%
unknown/invalid 3
 
< 0.1%
2025-04-29T13:53:38.394364image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 94007
30.3%
a 61045
19.7%
l 61045
19.7%
F 32965
 
10.6%
m 32965
 
10.6%
M 28077
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 310143
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 94007
30.3%
a 61045
19.7%
l 61045
19.7%
F 32965
 
10.6%
m 32965
 
10.6%
M 28077
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 310143
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 94007
30.3%
a 61045
19.7%
l 61045
19.7%
F 32965
 
10.6%
m 32965
 
10.6%
M 28077
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 310143
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 94007
30.3%
a 61045
19.7%
l 61045
19.7%
F 32965
 
10.6%
m 32965
 
10.6%
M 28077
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

age
Text

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:38.556288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.026111885
Min length6

Characters and Unicode

Total characters428909
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0-10)
2nd row[30-40)
3rd row[40-50)
4th row[50-60)
5th row[60-70)
ValueCountFrequency (%)
70-80 15589
25.5%
60-70 13423
22.0%
80-90 10408
17.0%
50-60 10382
17.0%
40-50 5747
 
9.4%
30-40 2268
 
3.7%
90-100 1697
 
2.8%
20-30 995
 
1.6%
10-20 433
 
0.7%
0-10 103
 
0.2%
2025-04-29T13:53:38.889260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 123787
28.9%
[ 61045
14.2%
- 61045
14.2%
) 61045
14.2%
7 29012
 
6.8%
8 25997
 
6.1%
6 23805
 
5.6%
5 16129
 
3.8%
9 12105
 
2.8%
4 8015
 
1.9%
Other values (3) 6924
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 428909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 123787
28.9%
[ 61045
14.2%
- 61045
14.2%
) 61045
14.2%
7 29012
 
6.8%
8 25997
 
6.1%
6 23805
 
5.6%
5 16129
 
3.8%
9 12105
 
2.8%
4 8015
 
1.9%
Other values (3) 6924
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 428909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 123787
28.9%
[ 61045
14.2%
- 61045
14.2%
) 61045
14.2%
7 29012
 
6.8%
8 25997
 
6.1%
6 23805
 
5.6%
5 16129
 
3.8%
9 12105
 
2.8%
4 8015
 
1.9%
Other values (3) 6924
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 428909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 123787
28.9%
[ 61045
14.2%
- 61045
14.2%
) 61045
14.2%
7 29012
 
6.8%
8 25997
 
6.1%
6 23805
 
5.6%
5 16129
 
3.8%
9 12105
 
2.8%
4 8015
 
1.9%
Other values (3) 6924
 
1.6%

weight
Text

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:38.998715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length1
Mean length1.214104349
Min length1

Characters and Unicode

Total characters74115
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?
ValueCountFrequency (%)
59160
96.9%
75-100 797
 
1.3%
50-75 502
 
0.8%
100-125 387
 
0.6%
125-150 79
 
0.1%
25-50 62
 
0.1%
0-25 25
 
< 0.1%
150-175 23
 
< 0.1%
175-200 8
 
< 0.1%
200 2
 
< 0.1%
2025-04-29T13:53:39.300993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 59160
79.8%
0 3079
 
4.2%
5 2549
 
3.4%
[ 1883
 
2.5%
- 1883
 
2.5%
) 1883
 
2.5%
1 1783
 
2.4%
7 1330
 
1.8%
2 563
 
0.8%
> 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74115
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 59160
79.8%
0 3079
 
4.2%
5 2549
 
3.4%
[ 1883
 
2.5%
- 1883
 
2.5%
) 1883
 
2.5%
1 1783
 
2.4%
7 1330
 
1.8%
2 563
 
0.8%
> 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74115
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 59160
79.8%
0 3079
 
4.2%
5 2549
 
3.4%
[ 1883
 
2.5%
- 1883
 
2.5%
) 1883
 
2.5%
1 1783
 
2.4%
7 1330
 
1.8%
2 563
 
0.8%
> 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74115
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 59160
79.8%
0 3079
 
4.2%
5 2549
 
3.4%
[ 1883
 
2.5%
- 1883
 
2.5%
) 1883
 
2.5%
1 1783
 
2.4%
7 1330
 
1.8%
2 563
 
0.8%
> 2
 
< 0.1%

admission_type_id
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.031468589
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:39.448400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.451984428
Coefficient of variation (CV)0.7147461869
Kurtosis1.865805886
Mean2.031468589
Median Absolute Deviation (MAD)0
Skewness1.577676147
Sum124011
Variance2.108258778
MonotonicityNot monotonic
2025-04-29T13:53:39.603532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 32286
52.9%
3 11254
 
18.4%
2 11120
 
18.2%
6 3217
 
5.3%
5 2959
 
4.8%
8 187
 
0.3%
7 14
 
< 0.1%
4 8
 
< 0.1%
ValueCountFrequency (%)
1 32286
52.9%
2 11120
 
18.2%
3 11254
 
18.4%
4 8
 
< 0.1%
5 2959
 
4.8%
ValueCountFrequency (%)
8 187
 
0.3%
7 14
 
< 0.1%
6 3217
5.3%
5 2959
4.8%
4 8
 
< 0.1%

discharge_disposition_id
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.699074453
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:39.823601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.250001167
Coefficient of variation (CV)1.419274262
Kurtosis6.096154818
Mean3.699074453
Median Absolute Deviation (MAD)0
Skewness2.575686123
Sum225810
Variance27.56251225
MonotonicityNot monotonic
2025-04-29T13:53:40.014330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 36154
59.2%
3 8360
 
13.7%
6 7815
 
12.8%
18 2202
 
3.6%
2 1267
 
2.1%
22 1172
 
1.9%
11 975
 
1.6%
5 702
 
1.1%
25 587
 
1.0%
4 492
 
0.8%
Other values (15) 1319
 
2.2%
ValueCountFrequency (%)
1 36154
59.2%
2 1267
 
2.1%
3 8360
 
13.7%
4 492
 
0.8%
5 702
 
1.1%
ValueCountFrequency (%)
28 82
 
0.1%
27 2
 
< 0.1%
25 587
1.0%
24 34
 
0.1%
23 229
 
0.4%

admission_source_id
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.759325088
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:40.212953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum22
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.07619424
Coefficient of variation (CV)0.7077555404
Kurtosis1.719839992
Mean5.759325088
Median Absolute Deviation (MAD)0
Skewness1.03165621
Sum351578
Variance16.61535948
MonotonicityNot monotonic
2025-04-29T13:53:40.439248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7 34410
56.4%
1 17733
29.0%
17 4124
 
6.8%
4 1919
 
3.1%
6 1345
 
2.2%
2 681
 
1.1%
5 527
 
0.9%
3 124
 
0.2%
20 92
 
0.2%
9 65
 
0.1%
Other values (5) 25
 
< 0.1%
ValueCountFrequency (%)
1 17733
29.0%
2 681
 
1.1%
3 124
 
0.2%
4 1919
 
3.1%
5 527
 
0.9%
ValueCountFrequency (%)
22 8
 
< 0.1%
20 92
 
0.2%
17 4124
6.8%
14 1
 
< 0.1%
11 1
 
< 0.1%

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.388647719
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:40.625004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.984864328
Coefficient of variation (CV)0.6801330432
Kurtosis0.8769941521
Mean4.388647719
Median Absolute Deviation (MAD)2
Skewness1.141522925
Sum267905
Variance8.909415059
MonotonicityNot monotonic
2025-04-29T13:53:40.790275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 10719
17.6%
2 10284
16.8%
1 8590
14.1%
4 8313
13.6%
5 5987
9.8%
6 4561
7.5%
7 3488
 
5.7%
8 2596
 
4.3%
9 1787
 
2.9%
10 1383
 
2.3%
Other values (4) 3337
 
5.5%
ValueCountFrequency (%)
1 8590
14.1%
2 10284
16.8%
3 10719
17.6%
4 8313
13.6%
5 5987
9.8%
ValueCountFrequency (%)
14 633
1.0%
13 747
1.2%
12 840
1.4%
11 1117
1.8%
10 1383
2.3%
Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:40.903063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.60398067
Min length1

Characters and Unicode

Total characters97915
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?
ValueCountFrequency (%)
24175
39.6%
mc 19391
31.8%
hm 3744
 
6.1%
sp 3030
 
5.0%
bc 2754
 
4.5%
md 2190
 
3.6%
cp 1523
 
2.5%
un 1453
 
2.4%
cm 1185
 
1.9%
og 623
 
1.0%
Other values (7) 977
 
1.6%
2025-04-29T13:53:41.244839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 26888
27.5%
C 25023
25.6%
? 24175
24.7%
P 4945
 
5.1%
H 3838
 
3.9%
S 3059
 
3.1%
B 2754
 
2.8%
D 2525
 
2.6%
U 1453
 
1.5%
N 1453
 
1.5%
Other values (5) 1802
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 26888
27.5%
C 25023
25.6%
? 24175
24.7%
P 4945
 
5.1%
H 3838
 
3.9%
S 3059
 
3.1%
B 2754
 
2.8%
D 2525
 
2.6%
U 1453
 
1.5%
N 1453
 
1.5%
Other values (5) 1802
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 26888
27.5%
C 25023
25.6%
? 24175
24.7%
P 4945
 
5.1%
H 3838
 
3.9%
S 3059
 
3.1%
B 2754
 
2.8%
D 2525
 
2.6%
U 1453
 
1.5%
N 1453
 
1.5%
Other values (5) 1802
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 26888
27.5%
C 25023
25.6%
? 24175
24.7%
P 4945
 
5.1%
H 3838
 
3.9%
S 3059
 
3.1%
B 2754
 
2.8%
D 2525
 
2.6%
U 1453
 
1.5%
N 1453
 
1.5%
Other values (5) 1802
 
1.8%
Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:41.455419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length33
Mean length8.625243673
Min length1

Characters and Unicode

Total characters526528
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowPediatrics-Endocrinology
2nd row?
3rd row?
4th row?
5th row?
ValueCountFrequency (%)
29890
49.0%
internalmedicine 8745
 
14.3%
emergency/trauma 4556
 
7.5%
family/generalpractice 4502
 
7.4%
cardiology 3250
 
5.3%
surgery-general 1842
 
3.0%
nephrology 989
 
1.6%
orthopedics 868
 
1.4%
orthopedics-reconstructive 741
 
1.2%
radiologist 690
 
1.1%
Other values (62) 4972
 
8.1%
2025-04-29T13:53:42.007529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 63115
 
12.0%
r 46200
 
8.8%
a 42787
 
8.1%
n 41160
 
7.8%
i 38062
 
7.2%
c 30078
 
5.7%
? 29890
 
5.7%
l 29361
 
5.6%
y 21023
 
4.0%
t 20511
 
3.9%
Other values (32) 164341
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 526528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 63115
 
12.0%
r 46200
 
8.8%
a 42787
 
8.1%
n 41160
 
7.8%
i 38062
 
7.2%
c 30078
 
5.7%
? 29890
 
5.7%
l 29361
 
5.6%
y 21023
 
4.0%
t 20511
 
3.9%
Other values (32) 164341
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 526528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 63115
 
12.0%
r 46200
 
8.8%
a 42787
 
8.1%
n 41160
 
7.8%
i 38062
 
7.2%
c 30078
 
5.7%
? 29890
 
5.7%
l 29361
 
5.6%
y 21023
 
4.0%
t 20511
 
3.9%
Other values (32) 164341
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 526528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 63115
 
12.0%
r 46200
 
8.8%
a 42787
 
8.1%
n 41160
 
7.8%
i 38062
 
7.2%
c 30078
 
5.7%
? 29890
 
5.7%
l 29361
 
5.6%
y 21023
 
4.0%
t 20511
 
3.9%
Other values (32) 164341
31.2%

num_lab_procedures
Real number (ℝ)

Distinct111
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.03076419
Minimum1
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:42.189708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum129
Range128
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.71393204
Coefficient of variation (CV)0.4581357643
Kurtosis-0.2513593021
Mean43.03076419
Median Absolute Deviation (MAD)13
Skewness-0.229903108
Sum2626813
Variance388.6391165
MonotonicityNot monotonic
2025-04-29T13:53:42.370463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1948
 
3.2%
43 1670
 
2.7%
44 1506
 
2.5%
45 1382
 
2.3%
38 1344
 
2.2%
40 1335
 
2.2%
46 1325
 
2.2%
42 1278
 
2.1%
41 1275
 
2.1%
39 1270
 
2.1%
Other values (101) 46712
76.5%
ValueCountFrequency (%)
1 1948
3.2%
2 649
 
1.1%
3 392
 
0.6%
4 236
 
0.4%
5 181
 
0.3%
ValueCountFrequency (%)
129 1
 
< 0.1%
118 1
 
< 0.1%
113 3
< 0.1%
109 3
< 0.1%
108 4
< 0.1%

num_procedures
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.34089606
Minimum0
Maximum6
Zeros28003
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:42.545996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.706563075
Coefficient of variation (CV)1.272703475
Kurtosis0.8487504009
Mean1.34089606
Median Absolute Deviation (MAD)1
Skewness1.313448655
Sum81855
Variance2.912357529
MonotonicityNot monotonic
2025-04-29T13:53:42.677819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 28003
45.9%
1 12378
20.3%
2 7632
 
12.5%
3 5703
 
9.3%
6 2976
 
4.9%
4 2517
 
4.1%
5 1836
 
3.0%
ValueCountFrequency (%)
0 28003
45.9%
1 12378
20.3%
2 7632
 
12.5%
3 5703
 
9.3%
4 2517
 
4.1%
ValueCountFrequency (%)
6 2976
 
4.9%
5 1836
 
3.0%
4 2517
 
4.1%
3 5703
9.3%
2 7632
12.5%

num_medications
Real number (ℝ)

Distinct74
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.0123024
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:42.864584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.097430024
Coefficient of variation (CV)0.5057005433
Kurtosis3.4015466
Mean16.0123024
Median Absolute Deviation (MAD)5
Skewness1.31011339
Sum977471
Variance65.56837299
MonotonicityNot monotonic
2025-04-29T13:53:43.077049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 3612
 
5.9%
12 3612
 
5.9%
14 3502
 
5.7%
11 3490
 
5.7%
15 3468
 
5.7%
10 3258
 
5.3%
16 3191
 
5.2%
17 2933
 
4.8%
9 2908
 
4.8%
18 2685
 
4.4%
Other values (64) 28386
46.5%
ValueCountFrequency (%)
1 154
 
0.3%
2 271
 
0.4%
3 550
0.9%
4 862
1.4%
5 1209
2.0%
ValueCountFrequency (%)
81 1
< 0.1%
75 2
< 0.1%
74 1
< 0.1%
72 1
< 0.1%
70 1
< 0.1%

number_outpatient
Real number (ℝ)

Zeros 

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3711851913
Minimum0
Maximum42
Zeros50954
Zeros (%)83.5%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:43.290188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.277205836
Coefficient of variation (CV)3.440885751
Kurtosis148.6296487
Mean0.3711851913
Median Absolute Deviation (MAD)0
Skewness8.877545881
Sum22659
Variance1.631254747
MonotonicityNot monotonic
2025-04-29T13:53:43.456366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 50954
83.5%
1 5198
 
8.5%
2 2145
 
3.5%
3 1195
 
2.0%
4 671
 
1.1%
5 322
 
0.5%
6 183
 
0.3%
7 90
 
0.1%
8 58
 
0.1%
9 49
 
0.1%
Other values (23) 180
 
0.3%
ValueCountFrequency (%)
0 50954
83.5%
1 5198
 
8.5%
2 2145
 
3.5%
3 1195
 
2.0%
4 671
 
1.1%
ValueCountFrequency (%)
42 1
< 0.1%
40 1
< 0.1%
36 2
< 0.1%
35 2
< 0.1%
33 2
< 0.1%

number_emergency
Real number (ℝ)

Skewed  Zeros 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2010156442
Minimum0
Maximum76
Zeros54209
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:43.619133image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9735067384
Coefficient of variation (CV)4.842940172
Kurtosis1314.061544
Mean0.2010156442
Median Absolute Deviation (MAD)0
Skewness24.90032669
Sum12271
Variance0.9477153697
MonotonicityNot monotonic
2025-04-29T13:53:43.801309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 54209
88.8%
1 4538
 
7.4%
2 1268
 
2.1%
3 456
 
0.7%
4 225
 
0.4%
5 127
 
0.2%
6 60
 
0.1%
7 45
 
0.1%
8 27
 
< 0.1%
9 18
 
< 0.1%
Other values (21) 72
 
0.1%
ValueCountFrequency (%)
0 54209
88.8%
1 4538
 
7.4%
2 1268
 
2.1%
3 456
 
0.7%
4 225
 
0.4%
ValueCountFrequency (%)
76 1
< 0.1%
64 1
< 0.1%
54 1
< 0.1%
46 1
< 0.1%
42 1
< 0.1%

number_inpatient
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6364321402
Minimum0
Maximum19
Zeros40539
Zeros (%)66.4%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:43.977768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.259008395
Coefficient of variation (CV)1.978228809
Kurtosis20.10529518
Mean0.6364321402
Median Absolute Deviation (MAD)0
Skewness3.571491953
Sum38851
Variance1.585102138
MonotonicityNot monotonic
2025-04-29T13:53:44.154581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 40539
66.4%
1 11659
 
19.1%
2 4604
 
7.5%
3 2094
 
3.4%
4 943
 
1.5%
5 496
 
0.8%
6 280
 
0.5%
7 158
 
0.3%
8 93
 
0.2%
9 63
 
0.1%
Other values (10) 116
 
0.2%
ValueCountFrequency (%)
0 40539
66.4%
1 11659
 
19.1%
2 4604
 
7.5%
3 2094
 
3.4%
4 943
 
1.5%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 4
< 0.1%
15 4
< 0.1%

diag_1
Text

Distinct661
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:44.514393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.177328201
Min length1

Characters and Unicode

Total characters193960
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)0.1%

Sample

1st row250.83
2nd row8
3rd row197
4th row414
5th row414
ValueCountFrequency (%)
428 4084
 
6.7%
414 4021
 
6.6%
786 2375
 
3.9%
410 2147
 
3.5%
486 2137
 
3.5%
427 1662
 
2.7%
715 1352
 
2.2%
491 1326
 
2.2%
434 1229
 
2.0%
682 1219
 
2.0%
Other values (651) 39493
64.7%
2025-04-29T13:53:45.021478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 33505
17.3%
2 23858
12.3%
8 22842
11.8%
5 22275
11.5%
7 17038
8.8%
1 16879
8.7%
0 14937
7.7%
6 13967
7.2%
9 11961
 
6.2%
3 10544
 
5.4%
Other values (3) 6154
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 193960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 33505
17.3%
2 23858
12.3%
8 22842
11.8%
5 22275
11.5%
7 17038
8.8%
1 16879
8.7%
0 14937
7.7%
6 13967
7.2%
9 11961
 
6.2%
3 10544
 
5.4%
Other values (3) 6154
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 193960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 33505
17.3%
2 23858
12.3%
8 22842
11.8%
5 22275
11.5%
7 17038
8.8%
1 16879
8.7%
0 14937
7.7%
6 13967
7.2%
9 11961
 
6.2%
3 10544
 
5.4%
Other values (3) 6154
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 193960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 33505
17.3%
2 23858
12.3%
8 22842
11.8%
5 22275
11.5%
7 17038
8.8%
1 16879
8.7%
0 14937
7.7%
6 13967
7.2%
9 11961
 
6.2%
3 10544
 
5.4%
Other values (3) 6154
 
3.2%

diag_2
Text

Distinct683
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:45.426044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.165582767
Min length1

Characters and Unicode

Total characters193243
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)0.2%

Sample

1st row?
2nd row250.43
3rd row157
4th row411
5th row411
ValueCountFrequency (%)
428 4080
 
6.7%
276 4006
 
6.6%
250 3639
 
6.0%
427 3015
 
4.9%
401 2301
 
3.8%
599 2005
 
3.3%
496 1971
 
3.2%
403 1746
 
2.9%
411 1550
 
2.5%
414 1539
 
2.5%
Other values (673) 35193
57.7%
2025-04-29T13:53:45.935052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 30822
15.9%
2 29903
15.5%
5 22744
11.8%
0 20564
10.6%
8 17151
8.9%
7 17062
8.8%
1 15695
8.1%
9 13056
6.8%
6 11961
 
6.2%
3 8555
 
4.4%
Other values (4) 5730
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 193243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 30822
15.9%
2 29903
15.5%
5 22744
11.8%
0 20564
10.6%
8 17151
8.9%
7 17062
8.8%
1 15695
8.1%
9 13056
6.8%
6 11961
 
6.2%
3 8555
 
4.4%
Other values (4) 5730
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 193243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 30822
15.9%
2 29903
15.5%
5 22744
11.8%
0 20564
10.6%
8 17151
8.9%
7 17062
8.8%
1 15695
8.1%
9 13056
6.8%
6 11961
 
6.2%
3 8555
 
4.4%
Other values (4) 5730
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 193243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 30822
15.9%
2 29903
15.5%
5 22744
11.8%
0 20564
10.6%
8 17151
8.9%
7 17062
8.8%
1 15695
8.1%
9 13056
6.8%
6 11961
 
6.2%
3 8555
 
4.4%
Other values (4) 5730
 
3.0%

diag_3
Text

Distinct730
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:46.289052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.112998608
Min length1

Characters and Unicode

Total characters190033
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)0.2%

Sample

1st row?
2nd row403
3rd row250
4th row250
5th rowV45
ValueCountFrequency (%)
250 6943
 
11.4%
401 4995
 
8.2%
276 3108
 
5.1%
428 2739
 
4.5%
427 2359
 
3.9%
414 2175
 
3.6%
496 1543
 
2.5%
403 1454
 
2.4%
585 1222
 
2.0%
272 1166
 
1.9%
Other values (720) 33341
54.6%
2025-04-29T13:53:46.790763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 30755
16.2%
4 29540
15.5%
5 24759
13.0%
0 23872
12.6%
7 15765
8.3%
1 14769
7.8%
8 14274
7.5%
9 10416
 
5.5%
6 9914
 
5.2%
3 8689
 
4.6%
Other values (4) 7280
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 190033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 30755
16.2%
4 29540
15.5%
5 24759
13.0%
0 23872
12.6%
7 15765
8.3%
1 14769
7.8%
8 14274
7.5%
9 10416
 
5.5%
6 9914
 
5.2%
3 8689
 
4.6%
Other values (4) 7280
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 190033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 30755
16.2%
4 29540
15.5%
5 24759
13.0%
0 23872
12.6%
7 15765
8.3%
1 14769
7.8%
8 14274
7.5%
9 10416
 
5.5%
6 9914
 
5.2%
3 8689
 
4.6%
Other values (4) 7280
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 190033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 30755
16.2%
4 29540
15.5%
5 24759
13.0%
0 23872
12.6%
7 15765
8.3%
1 14769
7.8%
8 14274
7.5%
9 10416
 
5.5%
6 9914
 
5.2%
3 8689
 
4.6%
Other values (4) 7280
 
3.8%

number_diagnoses
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.415136375
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:46.940730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.932361661
Coefficient of variation (CV)0.2605969147
Kurtosis-0.1075337942
Mean7.415136375
Median Absolute Deviation (MAD)1
Skewness-0.868942629
Sum452657
Variance3.734021591
MonotonicityNot monotonic
2025-04-29T13:53:47.086326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 29558
48.4%
5 6886
 
11.3%
7 6316
 
10.3%
8 6306
 
10.3%
6 6142
 
10.1%
4 3351
 
5.5%
3 1673
 
2.7%
2 623
 
1.0%
1 124
 
0.2%
16 26
 
< 0.1%
Other values (6) 40
 
0.1%
ValueCountFrequency (%)
1 124
 
0.2%
2 623
 
1.0%
3 1673
 
2.7%
4 3351
5.5%
5 6886
11.3%
ValueCountFrequency (%)
16 26
< 0.1%
15 5
 
< 0.1%
14 4
 
< 0.1%
13 10
 
< 0.1%
12 4
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:47.192003image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.053042837
Min length3

Characters and Unicode

Total characters186373
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNaN
2nd rowNaN
3rd rowNaN
4th rowNaN
5th rowNaN
ValueCountFrequency (%)
nan 57807
94.7%
norm 1574
 
2.6%
200 888
 
1.5%
300 776
 
1.3%
2025-04-29T13:53:47.463952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 117188
62.9%
a 57807
31.0%
0 3328
 
1.8%
> 1664
 
0.9%
o 1574
 
0.8%
r 1574
 
0.8%
m 1574
 
0.8%
2 888
 
0.5%
3 776
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 117188
62.9%
a 57807
31.0%
0 3328
 
1.8%
> 1664
 
0.9%
o 1574
 
0.8%
r 1574
 
0.8%
m 1574
 
0.8%
2 888
 
0.5%
3 776
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 117188
62.9%
a 57807
31.0%
0 3328
 
1.8%
> 1664
 
0.9%
o 1574
 
0.8%
r 1574
 
0.8%
m 1574
 
0.8%
2 888
 
0.5%
3 776
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 117188
62.9%
a 57807
31.0%
0 3328
 
1.8%
> 1664
 
0.9%
o 1574
 
0.8%
r 1574
 
0.8%
m 1574
 
0.8%
2 888
 
0.5%
3 776
 
0.4%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:47.564001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.931329347
Min length2

Characters and Unicode

Total characters178943
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNaN
2nd rowNaN
3rd rowNaN
4th rowNaN
5th rowNaN
ValueCountFrequency (%)
nan 50823
83.3%
8 4894
 
8.0%
norm 3015
 
4.9%
7 2313
 
3.8%
2025-04-29T13:53:47.837313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 104661
58.5%
a 50823
28.4%
> 7207
 
4.0%
8 4894
 
2.7%
o 3015
 
1.7%
r 3015
 
1.7%
m 3015
 
1.7%
7 2313
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 178943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 104661
58.5%
a 50823
28.4%
> 7207
 
4.0%
8 4894
 
2.7%
o 3015
 
1.7%
r 3015
 
1.7%
m 3015
 
1.7%
7 2313
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 178943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 104661
58.5%
a 50823
28.4%
> 7207
 
4.0%
8 4894
 
2.7%
o 3015
 
1.7%
r 3015
 
1.7%
m 3015
 
1.7%
7 2313
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 178943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 104661
58.5%
a 50823
28.4%
> 7207
 
4.0%
8 4894
 
2.7%
o 3015
 
1.7%
r 3015
 
1.7%
m 3015
 
1.7%
7 2313
 
1.3%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:47.936110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.7317225
Min length2

Characters and Unicode

Total characters166758
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowSteady
ValueCountFrequency (%)
no 49068
80.4%
steady 10994
 
18.0%
up 637
 
1.0%
down 346
 
0.6%
2025-04-29T13:53:48.214437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 49414
29.6%
N 49068
29.4%
S 10994
 
6.6%
t 10994
 
6.6%
e 10994
 
6.6%
a 10994
 
6.6%
d 10994
 
6.6%
y 10994
 
6.6%
U 637
 
0.4%
p 637
 
0.4%
Other values (3) 1038
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 166758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 49414
29.6%
N 49068
29.4%
S 10994
 
6.6%
t 10994
 
6.6%
e 10994
 
6.6%
a 10994
 
6.6%
d 10994
 
6.6%
y 10994
 
6.6%
U 637
 
0.4%
p 637
 
0.4%
Other values (3) 1038
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 166758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 49414
29.6%
N 49068
29.4%
S 10994
 
6.6%
t 10994
 
6.6%
e 10994
 
6.6%
a 10994
 
6.6%
d 10994
 
6.6%
y 10994
 
6.6%
U 637
 
0.4%
p 637
 
0.4%
Other values (3) 1038
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 166758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 49414
29.6%
N 49068
29.4%
S 10994
 
6.6%
t 10994
 
6.6%
e 10994
 
6.6%
a 10994
 
6.6%
d 10994
 
6.6%
y 10994
 
6.6%
U 637
 
0.4%
p 637
 
0.4%
Other values (3) 1038
 
0.6%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:48.314678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.056155295
Min length2

Characters and Unicode

Total characters125518
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 60099
98.5%
steady 841
 
1.4%
up 73
 
0.1%
down 32
 
0.1%
2025-04-29T13:53:48.625215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 60131
47.9%
N 60099
47.9%
S 841
 
0.7%
t 841
 
0.7%
e 841
 
0.7%
a 841
 
0.7%
d 841
 
0.7%
y 841
 
0.7%
U 73
 
0.1%
p 73
 
0.1%
Other values (3) 96
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 60131
47.9%
N 60099
47.9%
S 841
 
0.7%
t 841
 
0.7%
e 841
 
0.7%
a 841
 
0.7%
d 841
 
0.7%
y 841
 
0.7%
U 73
 
0.1%
p 73
 
0.1%
Other values (3) 96
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 60131
47.9%
N 60099
47.9%
S 841
 
0.7%
t 841
 
0.7%
e 841
 
0.7%
a 841
 
0.7%
d 841
 
0.7%
y 841
 
0.7%
U 73
 
0.1%
p 73
 
0.1%
Other values (3) 96
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 60131
47.9%
N 60099
47.9%
S 841
 
0.7%
t 841
 
0.7%
e 841
 
0.7%
a 841
 
0.7%
d 841
 
0.7%
y 841
 
0.7%
U 73
 
0.1%
p 73
 
0.1%
Other values (3) 96
 
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:48.872579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.0265378
Min length2

Characters and Unicode

Total characters123710
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 60622
99.3%
steady 402
 
0.7%
up 15
 
< 0.1%
down 6
 
< 0.1%
2025-04-29T13:53:49.196178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 60628
49.0%
N 60622
49.0%
S 402
 
0.3%
t 402
 
0.3%
e 402
 
0.3%
a 402
 
0.3%
d 402
 
0.3%
y 402
 
0.3%
U 15
 
< 0.1%
p 15
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123710
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 60628
49.0%
N 60622
49.0%
S 402
 
0.3%
t 402
 
0.3%
e 402
 
0.3%
a 402
 
0.3%
d 402
 
0.3%
y 402
 
0.3%
U 15
 
< 0.1%
p 15
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123710
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 60628
49.0%
N 60622
49.0%
S 402
 
0.3%
t 402
 
0.3%
e 402
 
0.3%
a 402
 
0.3%
d 402
 
0.3%
y 402
 
0.3%
U 15
 
< 0.1%
p 15
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123710
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 60628
49.0%
N 60622
49.0%
S 402
 
0.3%
t 402
 
0.3%
e 402
 
0.3%
a 402
 
0.3%
d 402
 
0.3%
y 402
 
0.3%
U 15
 
< 0.1%
p 15
 
< 0.1%
Other values (3) 18
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:49.300607image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.002850356
Min length2

Characters and Unicode

Total characters122264
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 60998
99.9%
steady 43
 
0.1%
up 3
 
< 0.1%
down 1
 
< 0.1%
2025-04-29T13:53:49.593278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 60999
49.9%
N 60998
49.9%
S 43
 
< 0.1%
t 43
 
< 0.1%
e 43
 
< 0.1%
a 43
 
< 0.1%
d 43
 
< 0.1%
y 43
 
< 0.1%
U 3
 
< 0.1%
p 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 60999
49.9%
N 60998
49.9%
S 43
 
< 0.1%
t 43
 
< 0.1%
e 43
 
< 0.1%
a 43
 
< 0.1%
d 43
 
< 0.1%
y 43
 
< 0.1%
U 3
 
< 0.1%
p 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 60999
49.9%
N 60998
49.9%
S 43
 
< 0.1%
t 43
 
< 0.1%
e 43
 
< 0.1%
a 43
 
< 0.1%
d 43
 
< 0.1%
y 43
 
< 0.1%
U 3
 
< 0.1%
p 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 60999
49.9%
N 60998
49.9%
S 43
 
< 0.1%
t 43
 
< 0.1%
e 43
 
< 0.1%
a 43
 
< 0.1%
d 43
 
< 0.1%
y 43
 
< 0.1%
U 3
 
< 0.1%
p 3
 
< 0.1%
Other values (3) 3
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:49.686695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.186026702
Min length2

Characters and Unicode

Total characters133446
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowSteady
ValueCountFrequency (%)
no 57959
94.9%
steady 2780
 
4.6%
up 188
 
0.3%
down 118
 
0.2%
2025-04-29T13:53:49.970548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 58077
43.5%
N 57959
43.4%
S 2780
 
2.1%
t 2780
 
2.1%
e 2780
 
2.1%
a 2780
 
2.1%
d 2780
 
2.1%
y 2780
 
2.1%
U 188
 
0.1%
p 188
 
0.1%
Other values (3) 354
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 58077
43.5%
N 57959
43.4%
S 2780
 
2.1%
t 2780
 
2.1%
e 2780
 
2.1%
a 2780
 
2.1%
d 2780
 
2.1%
y 2780
 
2.1%
U 188
 
0.1%
p 188
 
0.1%
Other values (3) 354
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 58077
43.5%
N 57959
43.4%
S 2780
 
2.1%
t 2780
 
2.1%
e 2780
 
2.1%
a 2780
 
2.1%
d 2780
 
2.1%
y 2780
 
2.1%
U 188
 
0.1%
p 188
 
0.1%
Other values (3) 354
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 58077
43.5%
N 57959
43.4%
S 2780
 
2.1%
t 2780
 
2.1%
e 2780
 
2.1%
a 2780
 
2.1%
d 2780
 
2.1%
y 2780
 
2.1%
U 188
 
0.1%
p 188
 
0.1%
Other values (3) 354
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:50.086393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000065525
Min length2

Characters and Unicode

Total characters122094
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 61044
> 99.9%
steady 1
 
< 0.1%
2025-04-29T13:53:50.480803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:50.592698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.45874355
Min length2

Characters and Unicode

Total characters150094
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSteady
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 53402
87.5%
steady 6833
 
11.2%
up 474
 
0.8%
down 336
 
0.6%
2025-04-29T13:53:50.889236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 53738
35.8%
N 53402
35.6%
S 6833
 
4.6%
t 6833
 
4.6%
e 6833
 
4.6%
a 6833
 
4.6%
d 6833
 
4.6%
y 6833
 
4.6%
U 474
 
0.3%
p 474
 
0.3%
Other values (3) 1008
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 53738
35.8%
N 53402
35.6%
S 6833
 
4.6%
t 6833
 
4.6%
e 6833
 
4.6%
a 6833
 
4.6%
d 6833
 
4.6%
y 6833
 
4.6%
U 474
 
0.3%
p 474
 
0.3%
Other values (3) 1008
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 53738
35.8%
N 53402
35.6%
S 6833
 
4.6%
t 6833
 
4.6%
e 6833
 
4.6%
a 6833
 
4.6%
d 6833
 
4.6%
y 6833
 
4.6%
U 474
 
0.3%
p 474
 
0.3%
Other values (3) 1008
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 53738
35.8%
N 53402
35.6%
S 6833
 
4.6%
t 6833
 
4.6%
e 6833
 
4.6%
a 6833
 
4.6%
d 6833
 
4.6%
y 6833
 
4.6%
U 474
 
0.3%
p 474
 
0.3%
Other values (3) 1008
 
0.7%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:50.984753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.378671472
Min length2

Characters and Unicode

Total characters145206
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 54568
89.4%
steady 5610
 
9.2%
up 529
 
0.9%
down 338
 
0.6%
2025-04-29T13:53:51.325716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 54906
37.8%
N 54568
37.6%
S 5610
 
3.9%
t 5610
 
3.9%
e 5610
 
3.9%
a 5610
 
3.9%
d 5610
 
3.9%
y 5610
 
3.9%
U 529
 
0.4%
p 529
 
0.4%
Other values (3) 1014
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 145206
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 54906
37.8%
N 54568
37.6%
S 5610
 
3.9%
t 5610
 
3.9%
e 5610
 
3.9%
a 5610
 
3.9%
d 5610
 
3.9%
y 5610
 
3.9%
U 529
 
0.4%
p 529
 
0.4%
Other values (3) 1014
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 145206
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 54906
37.8%
N 54568
37.6%
S 5610
 
3.9%
t 5610
 
3.9%
e 5610
 
3.9%
a 5610
 
3.9%
d 5610
 
3.9%
y 5610
 
3.9%
U 529
 
0.4%
p 529
 
0.4%
Other values (3) 1014
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 145206
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 54906
37.8%
N 54568
37.6%
S 5610
 
3.9%
t 5610
 
3.9%
e 5610
 
3.9%
a 5610
 
3.9%
d 5610
 
3.9%
y 5610
 
3.9%
U 529
 
0.4%
p 529
 
0.4%
Other values (3) 1014
 
0.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:51.417949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.001113932
Min length2

Characters and Unicode

Total characters122158
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 61028
> 99.9%
steady 17
 
< 0.1%
2025-04-29T13:53:51.679657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 61028
50.0%
o 61028
50.0%
S 17
 
< 0.1%
t 17
 
< 0.1%
e 17
 
< 0.1%
a 17
 
< 0.1%
d 17
 
< 0.1%
y 17
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61028
50.0%
o 61028
50.0%
S 17
 
< 0.1%
t 17
 
< 0.1%
e 17
 
< 0.1%
a 17
 
< 0.1%
d 17
 
< 0.1%
y 17
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61028
50.0%
o 61028
50.0%
S 17
 
< 0.1%
t 17
 
< 0.1%
e 17
 
< 0.1%
a 17
 
< 0.1%
d 17
 
< 0.1%
y 17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61028
50.0%
o 61028
50.0%
S 17
 
< 0.1%
t 17
 
< 0.1%
e 17
 
< 0.1%
a 17
 
< 0.1%
d 17
 
< 0.1%
y 17
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:51.795311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.278122696
Min length2

Characters and Unicode

Total characters139068
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 56617
92.7%
steady 4210
 
6.9%
up 149
 
0.2%
down 69
 
0.1%
2025-04-29T13:53:52.097517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 56686
40.8%
N 56617
40.7%
S 4210
 
3.0%
t 4210
 
3.0%
e 4210
 
3.0%
a 4210
 
3.0%
d 4210
 
3.0%
y 4210
 
3.0%
U 149
 
0.1%
p 149
 
0.1%
Other values (3) 207
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 56686
40.8%
N 56617
40.7%
S 4210
 
3.0%
t 4210
 
3.0%
e 4210
 
3.0%
a 4210
 
3.0%
d 4210
 
3.0%
y 4210
 
3.0%
U 149
 
0.1%
p 149
 
0.1%
Other values (3) 207
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 56686
40.8%
N 56617
40.7%
S 4210
 
3.0%
t 4210
 
3.0%
e 4210
 
3.0%
a 4210
 
3.0%
d 4210
 
3.0%
y 4210
 
3.0%
U 149
 
0.1%
p 149
 
0.1%
Other values (3) 207
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 56686
40.8%
N 56617
40.7%
S 4210
 
3.0%
t 4210
 
3.0%
e 4210
 
3.0%
a 4210
 
3.0%
d 4210
 
3.0%
y 4210
 
3.0%
U 149
 
0.1%
p 149
 
0.1%
Other values (3) 207
 
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:52.195809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.240085183
Min length2

Characters and Unicode

Total characters136746
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 57257
93.8%
steady 3638
 
6.0%
up 98
 
0.2%
down 52
 
0.1%
2025-04-29T13:53:52.526963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 57309
41.9%
N 57257
41.9%
S 3638
 
2.7%
t 3638
 
2.7%
e 3638
 
2.7%
a 3638
 
2.7%
d 3638
 
2.7%
y 3638
 
2.7%
U 98
 
0.1%
p 98
 
0.1%
Other values (3) 156
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 57309
41.9%
N 57257
41.9%
S 3638
 
2.7%
t 3638
 
2.7%
e 3638
 
2.7%
a 3638
 
2.7%
d 3638
 
2.7%
y 3638
 
2.7%
U 98
 
0.1%
p 98
 
0.1%
Other values (3) 156
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 57309
41.9%
N 57257
41.9%
S 3638
 
2.7%
t 3638
 
2.7%
e 3638
 
2.7%
a 3638
 
2.7%
d 3638
 
2.7%
y 3638
 
2.7%
U 98
 
0.1%
p 98
 
0.1%
Other values (3) 156
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 57309
41.9%
N 57257
41.9%
S 3638
 
2.7%
t 3638
 
2.7%
e 3638
 
2.7%
a 3638
 
2.7%
d 3638
 
2.7%
y 3638
 
2.7%
U 98
 
0.1%
p 98
 
0.1%
Other values (3) 156
 
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:52.621437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.010418544
Min length2

Characters and Unicode

Total characters122726
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 60879
99.7%
steady 158
 
0.3%
up 6
 
< 0.1%
down 2
 
< 0.1%
2025-04-29T13:53:52.997365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 60881
49.6%
N 60879
49.6%
S 158
 
0.1%
t 158
 
0.1%
e 158
 
0.1%
a 158
 
0.1%
d 158
 
0.1%
y 158
 
0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 60881
49.6%
N 60879
49.6%
S 158
 
0.1%
t 158
 
0.1%
e 158
 
0.1%
a 158
 
0.1%
d 158
 
0.1%
y 158
 
0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 60881
49.6%
N 60879
49.6%
S 158
 
0.1%
t 158
 
0.1%
e 158
 
0.1%
a 158
 
0.1%
d 158
 
0.1%
y 158
 
0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 60881
49.6%
N 60879
49.6%
S 158
 
0.1%
t 158
 
0.1%
e 158
 
0.1%
a 158
 
0.1%
d 158
 
0.1%
y 158
 
0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 6
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:53.129736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.001605373
Min length2

Characters and Unicode

Total characters122188
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 61018
> 99.9%
steady 23
 
< 0.1%
down 3
 
< 0.1%
up 1
 
< 0.1%
2025-04-29T13:53:53.428970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 61021
49.9%
N 61018
49.9%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%
D 3
 
< 0.1%
w 3
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 61021
49.9%
N 61018
49.9%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%
D 3
 
< 0.1%
w 3
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 61021
49.9%
N 61018
49.9%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%
D 3
 
< 0.1%
w 3
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 61021
49.9%
N 61018
49.9%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%
D 3
 
< 0.1%
w 3
 
< 0.1%
Other values (3) 5
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:53.530310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000131051
Min length2

Characters and Unicode

Total characters122098
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 61043
> 99.9%
steady 2
 
< 0.1%
2025-04-29T13:53:53.815866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 61043
50.0%
o 61043
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61043
50.0%
o 61043
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61043
50.0%
o 61043
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61043
50.0%
o 61043
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:53.905857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.001376034
Min length2

Characters and Unicode

Total characters122174
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 61024
> 99.9%
steady 21
 
< 0.1%
2025-04-29T13:53:54.212168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 61024
49.9%
o 61024
49.9%
S 21
 
< 0.1%
t 21
 
< 0.1%
e 21
 
< 0.1%
a 21
 
< 0.1%
d 21
 
< 0.1%
y 21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61024
49.9%
o 61024
49.9%
S 21
 
< 0.1%
t 21
 
< 0.1%
e 21
 
< 0.1%
a 21
 
< 0.1%
d 21
 
< 0.1%
y 21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61024
49.9%
o 61024
49.9%
S 21
 
< 0.1%
t 21
 
< 0.1%
e 21
 
< 0.1%
a 21
 
< 0.1%
d 21
 
< 0.1%
y 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61024
49.9%
o 61024
49.9%
S 21
 
< 0.1%
t 21
 
< 0.1%
e 21
 
< 0.1%
a 21
 
< 0.1%
d 21
 
< 0.1%
y 21
 
< 0.1%

examide
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:54.294924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters122090
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 61045
100.0%
2025-04-29T13:53:54.532749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 61045
50.0%
o 61045
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61045
50.0%
o 61045
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61045
50.0%
o 61045
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61045
50.0%
o 61045
50.0%

citoglipton
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:54.641146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters122090
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 61045
100.0%
2025-04-29T13:53:54.870323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 61045
50.0%
o 61045
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61045
50.0%
o 61045
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61045
50.0%
o 61045
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61045
50.0%
o 61045
50.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:54.990733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length3.456302727
Min length2

Characters and Unicode

Total characters210990
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowUp
3rd rowSteady
4th rowSteady
5th rowSteady
ValueCountFrequency (%)
no 28372
46.5%
steady 18584
30.4%
down 7282
 
11.9%
up 6807
 
11.2%
2025-04-29T13:53:55.396590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 35654
16.9%
N 28372
13.4%
S 18584
8.8%
t 18584
8.8%
e 18584
8.8%
a 18584
8.8%
d 18584
8.8%
y 18584
8.8%
D 7282
 
3.5%
w 7282
 
3.5%
Other values (3) 20896
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 210990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 35654
16.9%
N 28372
13.4%
S 18584
8.8%
t 18584
8.8%
e 18584
8.8%
a 18584
8.8%
d 18584
8.8%
y 18584
8.8%
D 7282
 
3.5%
w 7282
 
3.5%
Other values (3) 20896
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 210990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 35654
16.9%
N 28372
13.4%
S 18584
8.8%
t 18584
8.8%
e 18584
8.8%
a 18584
8.8%
d 18584
8.8%
y 18584
8.8%
D 7282
 
3.5%
w 7282
 
3.5%
Other values (3) 20896
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 210990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 35654
16.9%
N 28372
13.4%
S 18584
8.8%
t 18584
8.8%
e 18584
8.8%
a 18584
8.8%
d 18584
8.8%
y 18584
8.8%
D 7282
 
3.5%
w 7282
 
3.5%
Other values (3) 20896
9.9%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:55.489730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.027291342
Min length2

Characters and Unicode

Total characters123756
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 60622
99.3%
steady 415
 
0.7%
up 5
 
< 0.1%
down 3
 
< 0.1%
2025-04-29T13:53:55.758904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 60625
49.0%
N 60622
49.0%
S 415
 
0.3%
t 415
 
0.3%
e 415
 
0.3%
a 415
 
0.3%
d 415
 
0.3%
y 415
 
0.3%
U 5
 
< 0.1%
p 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123756
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 60625
49.0%
N 60622
49.0%
S 415
 
0.3%
t 415
 
0.3%
e 415
 
0.3%
a 415
 
0.3%
d 415
 
0.3%
y 415
 
0.3%
U 5
 
< 0.1%
p 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123756
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 60625
49.0%
N 60622
49.0%
S 415
 
0.3%
t 415
 
0.3%
e 415
 
0.3%
a 415
 
0.3%
d 415
 
0.3%
y 415
 
0.3%
U 5
 
< 0.1%
p 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123756
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 60625
49.0%
N 60622
49.0%
S 415
 
0.3%
t 415
 
0.3%
e 415
 
0.3%
a 415
 
0.3%
d 415
 
0.3%
y 415
 
0.3%
U 5
 
< 0.1%
p 5
 
< 0.1%
Other values (3) 9
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:55.862943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000524203
Min length2

Characters and Unicode

Total characters122122
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 61037
> 99.9%
steady 8
 
< 0.1%
2025-04-29T13:53:56.303698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 61037
50.0%
o 61037
50.0%
S 8
 
< 0.1%
t 8
 
< 0.1%
e 8
 
< 0.1%
a 8
 
< 0.1%
d 8
 
< 0.1%
y 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61037
50.0%
o 61037
50.0%
S 8
 
< 0.1%
t 8
 
< 0.1%
e 8
 
< 0.1%
a 8
 
< 0.1%
d 8
 
< 0.1%
y 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61037
50.0%
o 61037
50.0%
S 8
 
< 0.1%
t 8
 
< 0.1%
e 8
 
< 0.1%
a 8
 
< 0.1%
d 8
 
< 0.1%
y 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61037
50.0%
o 61037
50.0%
S 8
 
< 0.1%
t 8
 
< 0.1%
e 8
 
< 0.1%
a 8
 
< 0.1%
d 8
 
< 0.1%
y 8
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:56.418073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000065525
Min length2

Characters and Unicode

Total characters122094
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 61044
> 99.9%
steady 1
 
< 0.1%
2025-04-29T13:53:56.714373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:56.811836image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000131051
Min length2

Characters and Unicode

Total characters122098
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 61043
> 99.9%
steady 2
 
< 0.1%
2025-04-29T13:53:57.074414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 61043
50.0%
o 61043
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61043
50.0%
o 61043
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61043
50.0%
o 61043
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61043
50.0%
o 61043
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:57.167831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000065525
Min length2

Characters and Unicode

Total characters122094
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 61044
> 99.9%
steady 1
 
< 0.1%
2025-04-29T13:53:57.466075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61044
50.0%
o 61044
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

change
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:57.568614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters122090
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowCh
3rd rowCh
4th rowNo
5th rowCh
ValueCountFrequency (%)
no 32742
53.6%
ch 28303
46.4%
2025-04-29T13:53:57.804038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 32742
26.8%
o 32742
26.8%
C 28303
23.2%
h 28303
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 32742
26.8%
o 32742
26.8%
C 28303
23.2%
h 28303
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 32742
26.8%
o 32742
26.8%
C 28303
23.2%
h 28303
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 32742
26.8%
o 32742
26.8%
C 28303
23.2%
h 28303
23.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:57.897324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.771480056
Min length2

Characters and Unicode

Total characters169185
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes
ValueCountFrequency (%)
yes 47095
77.1%
no 13950
 
22.9%
2025-04-29T13:53:58.126862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 47095
27.8%
e 47095
27.8%
s 47095
27.8%
N 13950
 
8.2%
o 13950
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 169185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 47095
27.8%
e 47095
27.8%
s 47095
27.8%
N 13950
 
8.2%
o 13950
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 169185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 47095
27.8%
e 47095
27.8%
s 47095
27.8%
N 13950
 
8.2%
o 13950
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 169185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 47095
27.8%
e 47095
27.8%
s 47095
27.8%
N 13950
 
8.2%
o 13950
 
8.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:58.235553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.461921533
Min length2

Characters and Unicode

Total characters150288
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd rowNO
3rd rowNO
4th row>30
5th rowNO
ValueCountFrequency (%)
no 32847
53.8%
30 28198
46.2%
2025-04-29T13:53:58.505168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 32847
21.9%
O 32847
21.9%
3 28198
18.8%
0 28198
18.8%
> 21331
14.2%
< 6867
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 32847
21.9%
O 32847
21.9%
3 28198
18.8%
0 28198
18.8%
> 21331
14.2%
< 6867
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 32847
21.9%
O 32847
21.9%
3 28198
18.8%
0 28198
18.8%
> 21331
14.2%
< 6867
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 32847
21.9%
O 32847
21.9%
3 28198
18.8%
0 28198
18.8%
> 21331
14.2%
< 6867
 
4.6%

split
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
2025-04-29T13:53:58.628288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters305225
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrain
2nd rowtrain
3rd rowtrain
4th rowtrain
5th rowtrain
ValueCountFrequency (%)
train 61045
100.0%
2025-04-29T13:53:58.865062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 61045
20.0%
r 61045
20.0%
a 61045
20.0%
i 61045
20.0%
n 61045
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 305225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 61045
20.0%
r 61045
20.0%
a 61045
20.0%
i 61045
20.0%
n 61045
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 305225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 61045
20.0%
r 61045
20.0%
a 61045
20.0%
i 61045
20.0%
n 61045
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 305225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 61045
20.0%
r 61045
20.0%
a 61045
20.0%
i 61045
20.0%
n 61045
20.0%

load_date
Date

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size477.0 KiB
Minimum2025-04-29 00:00:00
Maximum2025-04-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-29T13:53:58.991071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T13:53:59.110742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)